Our current behavior is dropping all args / kwargs for both http and python, if user deployment function doesn't take any input. But in the meantime we didn't throw anything if user tries to invoke the function in python with actual args.
This PR adds this back, and added a bit special handling for http case with in-line comments.
Now given we directly return `ClassMethodNode` on `deployment_cls.bind()`, add a test to ensure chain of ClassMethod calls is consistent across ray dag and serve dag.
Note this only works on single replica, since if the class method mutates replica state, and there're multiple replicas running, replica states / result won't be consistent if request are routed to different ones.
dataset_shuffle_random_shuffle_1tb was previously failing due to OOM but has now passed on the last 4 runs due to changing the node type. These tests should be stable now, although we will want to look into the OOM issue later.
Users may want to provide Ray task option overrides for write tasks, e.g. having write tasks retried on application-level exceptions (retry_exceptions=True) or change the default number of retries (max_retries=8). This commit adds support for providing such task options for write tasks.
The exception of 'ValueError("Resource quantities >1 must be whole numbers.")' will be raised if the `num_cpus` > 1 and not an integer.
Co-authored-by: 黑驰 <senlin.zsl@antgroup.com>
Follow-up from #23908
Instead of manually deleting checkpoint paths after calling `to_directory()`, we should utilize `Checkpoint.as_directory()` when possible.
The total execution time for multi-stage operations being logged twice in the dataset stats is [confusing to users](https://github.com/ray-project/ray/issues/23915), making it seem like each stage in the operation took the same amount of time. This PR modifies the stats output for multi-stage operations, such that the total execution time is printed out once as a top-level op stats line, with the stats for each of the (sub)stages indented and devoid of the total execution time repeat.
This also opens the door for other op-level stats (e.g. peak memory utilization) and per-substage stats (e.g. total substage execution time).
This PR refactors ExecutionPlan to maintain complete stage lineage, even for eagerly computed datasets, while ensuring that block references are unlinked as early as possible in order to more eagerly release block memory. This PR is the final precursor to adding the actual out-of-band serialization APIs (PR 3/3).
The fully lineage has to be maintained, even for eagerly computed datasets, since the lineage is needed for out-of-band serialization of datasets.
Adds a content-type-agnostic partition parser with support for filtering files. Also adds some corner-case bug fixes and usability improvements for supporting more robust input path types.
We had unreported merge conflicts with DDPPO. This PR closes and combines #24092, #24035, #24030 and #23096
Co-authored-by: sven1977 <svenmika1977@gmail.com>
* Revert "Revert "[tune] Also interrupt training when SIGUSR1 received" (#24085)"
This reverts commit 00595653ed.
Failure in windows has been addressed by conditionally registering the signal handler if available.
Creates a zip of session_latest dir with test name and timestamp upon python test failure. Writes to dir specified by env var `RAY_TEST_FAILURE_LOGS_DIR`. Noop if env var does not exist.
Downstream consumer (e.g. CI) can upload all created artifacts in this dir. Thereby, PR submitters can more easily debug their CI failures, especially if they can't repro locally.
Limitations:
- a conftest.py file importing the main ray conftest.py needs to be present in same dir as test. This presents a challenge for e.g. dashboard tests which are highly scattered
This PR implements ray list tasks and ray list objects APIs.
NOTE: You can ignore the merge conflict for now. It is because the first PR was reverted. There's a fix PR open now.
Serve stores context state, including the `_INTERNAL_REPLICA_CONTEXT` and the `_global_client` in `api.py`. However, these data structures are referenced throughout the codebase, causing circular dependencies. This change introduces two new files:
* `context.py`
* Intended to expose process-wide state to internal Serve code as well as `api.py`
* Stores the `_INTERNAL_REPLICA_CONTEXT` and the `_global_client` global variables
* `client.py`
* Stores the definition for the Serve `Client` object, now called the `ServeControllerClient`
- Closes#23874 by fixing a typo ("num_gpus" -> "num-gpus").
- Adds end-to-end test logic confirming the fix.
- Adds end-to-end test logic confirming autoscaling with custom resources works.
- Slightly refines developer instructions.
- Deflakes test logic a bit by allowing for the event that the head pod changes its identity as the Ray cluster starts up.
Since remote calls provide no ordering guarantees, it could happen that `reconfigure` gets called before `is_allocated` Since `reconfigure` then runs the user initialization code, the replica actor could get blocked and never provide its allocation check.
This PR ensures that the allocation proof has been received before we run the replica initialization.
See dag layering summary in https://github.com/ray-project/ray/issues/24061
We need to cleanup and set right ray dag -> serve dag layering where `.bind()` can be called on `@serve.deployment` decorated class or func, but only returns raw Ray DAGNode type, executable by ray core and serve_dag is only available after serve-specific transformations.
Thus this PR removes exposed serve DAGNode type such as DeploymentNode.
It also removes the syntax of `class.bind().bind()` to return a `DeploymentMethodNode` that defaults to `__call__` to match same behavior in ray dag building.
When a `Trainer` is initialized with a run config and then passed into a `Tuner`, it is currently silently discarded and a default RunConfig is used. Instead we should use the run config in trainer if not overridden.
Add example for distributed pytorch geometric (graph learning) with Ray AIR
This only showcases distributed training, but with data small enough that it can be loaded in by each training worker individually. Distributed data ingest is out of scope for this PR.
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
For the purpose to provide an alternative option for running multiple actor instances in a Java worker process, and the eventual goal is to remove the original multi-worker-instances in one worker process implementation. we're proposing supporting parallel actor concept in Java. This feature enables that users could define some homogeneous parallel execution instances in an actor, and all instances hold one thread as the execution backend.
### Introduction
For the following example, we define a parallel actor with 10 parallelism. The backend actor has 10 concurrency groups for the parallel executions, it also means there're 10 threads for that.
We can access the instance by the instance handle, like:
```java
ParallelActorHandle<A> actor = ParallelActor.actor(A::new).setParallelism(10).remote();
ParallelInstance<A> instance = actor.getInstance(/*index=*/ 2);
Preconditions.checkNotNull(instance);
Ray.get(instance.task(A::incr, 1000000).remote()); // print 1000000
instance = actor.getInstance(/*index=*/ 2);
Preconditions.checkNotNull(instance);
Ray.get(instance.task(A::incr, 2000000).remote().get()); // print 3000000
instance = actor.getInstance(/*index=*/ 3);
Preconditions.checkNotNull(instance);
Ray.get(instance.task(A::incr, 2000000).remote().get()); // print 2000000
```
### Limitation
- It doesn't support concurrency group on a parallel actor yet.
Co-authored-by: Kai Yang <kfstorm@outlook.com>
Ray Tune currently gracefully stops training on SIGINT. However, the Ray core worker prevents SIGINT (and SIGTERM) to be processed by child tasks, which means that Ray Tune runs that are started in remote tasks (e.g. via Ray client) cannot be gracefully interrupted.
In k8s-based cloud tests that used the Ray client to kick off a Ray Tune run, this lead to test flakiness, as final experiment state could not be gracefully persisted to cloud storage.
This PR adds support for SIGUSR1 in addition to SIGINT to interrupt training gracefully.
